Automated driving system (ADS) deployment requires rigorous validation across safety–critical vehicle–pedestrian interactions, yet real-world datasets rarely capture high-risk scenarios while simulation platforms lack realistic behavior. In response, this study proposes a three-stage framework that combines real-world grounding with adaptive simulation to generate behaviorally realistic safety–critical scenarios at scale. Stage 1 pre-trains multi-agent state-space Transformer-enhanced DDPG (MA-SST-DDPG) agents on real-world safety–critical data to learn human-like interactive evasive behaviors through data-driven learning. Stage 2 deploys pre-trained multi-agents in CARLA for online reinforcement learning to generalize across diverse scenarios, integrating real-world knowledge with simulation experience to produce a refined MA-SST-DDPG model. Stage 3 uses CARLA with the refined model to generate over 198,000 high-resolution interaction episodes from eight intersection scenarios, culminating in the Vehicle–Pedestrian Safety-Critical Interaction (VPSCI) dataset. The Refined MA-SST-DDPG model outperformed baseline methods in reproducing realistic evasive behaviors, achieving the lowest trajectory errors (ADE = 0.072 m, FDE = 0.142 m). Statistical comparison confirmed distributional equivalence between the generated and real-world data in both conflict severity and behavioral response. A Turing test confirmed that the three-stage framework generated evasive behaviors were indistinguishable from real-world interactions. The generated data revealed realistic behavioral patterns: conflict rates rose with speed, and pedestrian yielding increased with vehicle proximity and speed—trends matching real-world observations. These results demonstrate the framework’s effectiveness in producing high-fidelity safety–critical data, offering valuable sources for the development of ADS and simulation-based safety evaluations.
As a special nonlinear scalar function, the oriented distance function has been widely used in the scalarization of multiobjective optimization problems (MOPs). However, its non-differentiability makes it difficult to construct efficient algorithms based on its theoretical results. In order to solve this problem, this paper investigates smoothing approximations of the oriented distance function and establishes the smoothing characterizations and smoothing algorithms for nonsmooth MOPs. First, two types of smoothing approximations for the oriented distance function are presented and their corresponding gradient vectors, Hessian matrices and related properties are derived. Subsequently, building upon these smoothing expressions, a smoothing numerical optimization model is formulated, and its theoretical connection to the nonsmooth MOPs solutions is established. Finally, a smoothing projection gradient algorithm for the original problem is developed, and numerical results show the effectiveness of the proposed methods.
Curcumin (CUR) is widely employed in the fields of foods, spices, and pharmaceuticals. Accurate detection of CUR content is crucial for supervising its quality. In this study, a 3D Zn-based metal-organic framework (MOF) was synthesized by using 1,2,3,5-phenyltetracarboxylic acid ligand (H(4)ptca), [Zn-2(ptca)(H2O)(2)]n (Zn-1). It is important to note that the emission peak of Zn-1 overlapped with the excitation spectrum of CUR. Based on Fo & uml;rster resonance energy transfer process (FRET), Zn-1 as an energy donor reduced its own emission intensity, while CUR as an energy acceptor significantly enhanced the fluorescence, elucidating an effective construction strategy of ratiometric probes. Accordingly, a dual-emission fluorescent probe has been developed for detecting CUR, and its detection mechanism was investigated in detail. Zn-1 exhibited an ultra-high sensitivity and excellent selectivity toward CUR, the limit of detection reached 4.68 & times; 10(-9) M. Combining with a portable sensing device and a self-designed smart-phone app, Zn-1 could be suitable for real-time identification of CUR in actual samples.
The oxygen reduction reaction (ORR) plays a central role in determining the efficiency and durability of a broad range of electrochemical energy conversion technologies, while its intrinsically slow kinetics and multistep reaction nature remain key challenges. Transition metal-based heterogeneous electrocatalysts, particularly those incorporating Fe, Mn, Co, Ni, and Cu, have been extensively investigated as ORR active systems owing to their earth abundance, diverse coordination environments, and tunable electronic structures. Recent studies show that ORR on these catalysts can diverge into two-electron and four-electron pathways from a common O2 reactant, with pathway selectivity governed by atomic-scale coordination and electronic effects. This review summarizes current understanding of ORR mechanisms on transition metal-based catalysts, with a focus on how atomic-scale structural features are related to reaction activity, selectivity, and stability under different electrolyte conditions. Representative applications in metal-air batteries, fuel cells, and electrochemical hydrogen peroxide production are also discussed to illustrate how mechanistic insights are reflected in practical electrochemical systems. By distilling shared mechanistic features across different systems, this review provides a coherent framework for understanding and guiding ORR catalyst design.
Engineering advanced S-scheme heterojunction photocatalysts represents a prospective strategy for efficient antibiotic-contaminated wastewater decontamination. However, the practical realization of such systems is hindered by difficulties in achieving seamless interfacial integration and precise control over charge-carrier dynamics. Herein, we proposed a shell-core 0D/2D Mn0.5 Cd0.5 S/C3 N5 S-scheme heterojunction with compact interfacial contact, synthesized by in-situ solvothermal growth of Mn0.5 Cd0.5 S nanodots on C3 N5 nanosheets. This optimized Mn0.5 Cd0.5 S/C3 N5 heterojunction performs extraordinary catalytic performance and enables approximately 1.3- and 3.2-fold tetracycline abatement rate greater than that for Mn0.5 Cd0.5 S and C3 N5 , respectively, which arises from the synergy of efficient spatial photo-carrier separation and well preserved great redox capacity of the heterojunction enabled by the S-scheme mechanism. Mechanistic validation was achieved through systematic characterizations and computational analyses. This study advances the rational design of shell-core S-scheme heterojunctions for photocatalytic antibiotic wastewater treatment. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
